Building Services Engineering Research & Technology

In its simplest form, a Closed Cavity Façade (CCF) consists of a double or triple glazing unit (DGU or TGU) on the inner layer and single glazing on the outer one, forming a sealed non-ventilated cavity with an automated shading device in between. Given its dynamic behaviour, this technology can dynamically control the flow of solar energy and light penetrating the building. Using EnergyPlus and IDA ICE, several CCF configurations were investigated and compared to the baseline (TGU). MATELab, an office-like test facility at the University of Cambridge, was used as the model, which was beforehand experimentally validated. The results show extensive benefits of CCFs compared to traditional TGU systems, in terms of thermal performance and occupants’ comfort. The CCF configurations investigated led to an improvement of energy performance in the range of 18–37% compared to the traditional TGU, depending on the CCF configuration and the climate while a previous study, using CCF configurations with DGU as inner skin, revealed an improvement of energy performance in the range of 22–41% compared to the conventional DGU. Further investigation showed that glass coatings and solar shading characteristics play an important role in cutting down overheating phenomenon while increasing occupants’ comfort. Practical application: Governments are making ever more stringent energy regulations for the building industry aiming to reduce energy consumption and carbon emissions. At the same time, building owners and architects are looking at cost-effective solutions for the long-term performance of buildings while tenants/occupants are more than ever aware of the fact that building comfort increases well-being and productivity. In all these regards, this work focuses and accentuates that substantial improvements can be achieved by designing and using suitable configurations of the innovative Closed Cavity Façade according to the climatic conditions of each location. The results presented indicate that there is much potential in improving the energy and comfort performance of a building, raising awareness to help deploy innovative glazing technologies.

Smart thermostats allow continuous learning, remote scheduling and control of indoor temperature. This paper empirically evaluates indoor environmental conditions, occupant experiences and prevalence of summertime overheating in three low-energy dwellings with smart thermostats and compares the results with three similar dwellings with standard programmable thermostats. The study uses building performance evaluation methods combining time-series data on temperature, relative humidity and window opening with survey data on occupant perception of thermal comfort and heating control over the period 2019–2020. While there was little difference observed in the measured and perceived indoor temperatures between dwellings with and without smart thermostats, the six dwellings were different in the way they heated their homes and controlled their indoor environment. A wide indoor temperature range of 16^oC–22^oC was observed in dwellings with smart thermostats during the heating season. The majority of dwellings also experienced summertime overheating with temperatures in bedrooms going up to 34^oC. Individual heating preferences dominated the use of smart or standard thermostats ranging from Cool Conserver, On-off Switcher to On-demand Sizzler. It is vital that energy models consider a range of heating preferences to avoid a gap between expectation and reality. Practical application: Actual in-use performance of dwellings with smart thermostats is necessary for their large-scale deployment. A wide range of thermostat behaviours are documented; therefore, it is vital that energy models consider a range of heating preferences to minimise the gap between energy models and reality. As smart home appliances and controls become more commonplace, the findings demonstrate their need for resident training and trouble-shooting support to ensure smart thermostats deliver their expected benefits. Since most of the case study dwellings experienced summertime overheating, it is also vital that building design tackles overheating through passive measures.

Fire poses a significant risk across industrial and domestic settings, especially to firefighters who must tackle the blaze. Current technology for detection in indoor environments are smoke detectors and flame detectors. However, these detectors have several limitations during the ignition phase of a fire and propagation. These systems cannot detect an exact position of the fire nor how the fire is spreading or its size, all of which is necessary information for fire services when dealing with these incidents. A potential solution is to use artificial intelligence techniques such as computer vision, which has shown the potential to detect and recognise objects and activities in indoor spaces. This study aims to develop a vision-based fire and smoke detection system. A deep learning technique that incorporates convolutional neural networks (CNN) was utilised to develop the real-time detection approach that can potentially provide necessary information for fire services, including identifying the position and size of the fire and how the fire spreads. A transfer learning approach using a pre-trained model was used to train the detector. Based on the detection and recognition tests using indoor fire and smoke videos, results indicated that the fire detection achieved up to 92.37% correct detections while the smoke detection did not perform as well. Hence, further improvement and evaluation of the detection approach will be conducted in future work, focusing on the impact of different parameters such as the detection model, building type, indoor space size and positioning of the detection camera. The present study provides an insight into the capabilities and potential applications of the concept.

This paper describes a heat pump investigation for GreenSCIES (GS), a fifth Generation district heating and cooling (5DHC) network in Islington, London. The paper describes the GreenSCIES concept integrating Mobility, Power and Heat into a Smart Local Energy System (SLES). At the heart of the system is an ultra-low temperature ambient loop network, which permits bi-directional flow within the pipes to allow energy exchange between heating and cooling customers at different times and in different locations, depending on where demand is at any given time. An existing data centre provides the primary source of waste heat for the scheme. Heat pumps in distributed energy centres are utilised to amplify the temperature of the ambient loop to deliver heat in connected buildings. The energy centres integrate heat pumps with building-mounted solar photovoltaic (PV) systems and electric vehicle (EV) charging points. The paper provides an overview of the integrated SLES concept, focussing on the heat pump selection and the short and long-term thermal storage options designed for the scheme. The results show that even the smaller constructible ‘New River’ scheme will save 5,000 tons of CO₂e annually. This will tend to 100% as the grid decarbonise further. Therefore, the GS SLES concept applied to urban areas could deliver significant carbon emission savings in the UK and elsewhere.

Practical application: Project GreenSCIES, is a detailed design study to develop a Smart, Local Energy System (SLES) for a large community in the London Borough of Islington. Our consortium have developed an innovative SLES concept, centred around a fifth generation district heating and cooling network. The GS ambient loop systems have negligible losses and much greater efficiencies than traditional district heat networks. As recognised by the UK Government’s Heat and Buildings Strategy, ambient loop systems should be considered where large-scale neighbourhood regeneration occurs. The proposed SLES concept applied to wider urban areas could deliver significant carbon emission savings in the UK.

Recovering waste heat from urban infrastructures is becoming increasingly important as the UK strives to decarbonise heat, which remains one of the main challenges in the transition towards net zero. The Bunhill Waste Heat Recovery (WHR) System represents a first of its kind scheme that will recover waste energy from a ventilation shaft of the London Underground (LU) transport network. The system is based upon the installation of a heat recovery heat exchanger that consists of cooling coils and a reversible fan. The coils are connected to a heat pump that supplies low-carbon thermal energy to the Bunhill Heat Network in Central London. One particularly important aspect of the Bunhill WHR system is its ability to operate in a way that not only provides heating to the local heat network, but can also simultaneously supply cooled air to the LU tunnels depending on the operation of the reversible fan. The current paper estimates the potential cooling benefit that could be achieved with the WHR system based upon the development of a mathematical model. The model is able to predict the condition of the coil surface according to air inlet parameters, and this is used to calculate the latent and sensible cooling loads, which are applied to simulate how the system affects the local tunnel environment, with peak temperature reductions of up to 7.2 K being estimated for adjacent stations in 2030. The results from the investigation are presented together with recommendations for further development and future deployment of heat recovery from metro systems, as this technology could be applied across London and elsewhere to deliver significant carbon and cost savings while improving the thermal environment of railway tunnels.

In residential air conditioning systems, outdoor units are often installed in the recesses of building facades and shaded by louvres; however, different unit installation positions and louvre blade angles affect the thermal environment around the outdoor unit and the energy efficiency ratio (EER) of the air conditioner. In this study, the effects of the outdoor unit installation position and louvre blade angle on the EER when a single outdoor unit was installed in a recess were investigated by experiments on a 1.5 hp air conditioner (rated power of the air conditioner is 3.5 kW), and the influence of the spacing and angle between two outdoor units on the air conditioner EER when two outdoor units were installed in the same recess was explored. The results of the research indicate that when a single outdoor unit is installed in the recess, the EER increases with an increase in the distance between the inlet of the outdoor unit and the wall. To meet the three-level standard of air conditioner EERs, the distance between the inlet and wall needs to be greater than 300 mm. The EER first increased and then decreased slowly with the increase in the distance between the outdoor unit outlet and louvre; thus, the distance between the outlet and louvre should not be less than 300 mm. The EER first increased and then decreased with the increase in the blade angle, and thus, the blade angle should not be greater than 20°. When two outdoor units are installed in the same recess, each installation mode, “horizontal installation” (same height and collinear), “perpendicular installation” (same height and perpendicular), “angle installation” (same height and obtuse angle), and “up and down parallel installation” (different heights and parallel), has an optimum installation distance and angle.

A comprehensive understanding of occupant comfort in mixed-mode (MM) buildings is crucial for the design of MM buildings which are being proposed as a low-energy solution to combat the global warming without compromising comfort. Current comfort standards are mainly for naturally ventilated (NV) or air-conditioned (AC) buildings, and there is a significant gap in standards for MM buildings. With comfort databases playing a major role in the development of thermal comfort models, the recently published ASHRAE Global Thermal Comfort Database II is utilised, in this research, to investigate the thermal sensation and occupant’s behavioural adaptations in MM buildings in temperate oceanic climates and to develop an adaptive thermal comfort model based on the outdoor environmental conditions. The Fanger's PMV model was found to underpredict the actual thermal sensation of the occupants while the occupant adaptivity was found to be lower than that predicted by the adaptive models of Standard 55 and EN15251/EN 16798-1. Furthermore, based on the results of this study and the various impediments faced, recommendations are proposed for future comfort surveys so that more detailed and conclusive studies can be conducted for wider applications using open-source thermal comfort databases.